Text mining can be used to extract latent semantic content from collections of structured and unstructured text. Data visualization can then be used to model the extracted semantic content, which transforms numeric or textual data into graphical data to assist users in understanding underlying principles. For example, clusters group related sets of concepts into a single graphical element that can be mapped into the graphical screen. When mapped into multi-dimensional space, the spatial orientation of the clusters can reflect similarities and relatedness of clusters. However, artificially mapping the clusters into a three-dimensional scene or a two-dimensional screen can present potential problems. For instance, a viewer could misinterpret dependent relationships between discrete clusters displayed adjacently or erroneously interpret dependent variables as independent and independent variables as dependent. Similarly, a screen of densely-packed clusters can be difficult to understand and navigate, particularly where textual labels are annotated to overlie the cluster directly. Other factors can further complicate the perception of visualized data, such as described in R. E. Horn, “Visual Language: Global Communication for the 21st Century,” Ch. 3, MacroVU Press (1998), the disclosure of which is incorporated by reference.
Moreover, data visualization is constrained by the physical limits of the screen system used. Two-dimensional visualized data can be readily displayed, yet visualized data of greater dimensionality must be artificially projected into two-dimensions when displayed on conventional display devices. Careful use of color, shape and temporal attributes can simulate multiple dimensions, but comprehension and usability become difficult as additional layers of modeling are artificially grafted into a two-dimensional screen space and display density increases. In addition, large sets of data, such as email stores, document archives and databases, can be content rich and can yield large sets of clusters that result in a complex screen. Display, however, is limited and large cluster sets can appear crowded and dense, thereby hindering understandability. To aid navigation through the display, the cluster sets can be combined, abstracted or manipulated to simplify presentation, but semantic content can be lost or skewed.
Moreover, complex graphical data can be difficult to comprehend when displayed alone and without textual references to underlying content. The user is forced to remember “landmark” clusters and similar visual cues by screen position alone, which can be particularly difficult with large cluster sets. The visualized data can be annotated with text, such as cluster labels, to aid comprehension and usability. However, annotating text directly into a graphical display can be cumbersome, particularly where the clusters are densely packed and cluster labels overlay or occlude the display. A more subtle problem occurs when the screen is displaying a two-dimensional projection of three-dimensional data and the text is annotated within the two-dimensional space. Relabeling the text based on the two-dimensional representation can introduce misinterpretations of the three-dimensional data when the display is reoriented. Also, reorienting the display during data evaluation can cause a shuffling of the displayed clusters and a loss of user orientation. Furthermore, navigation within such a display can be unintuitive and cumbersome, as cluster placement is driven by available display and the provisioning of labels necessarily overlays or intersects placed clusters.
Therefore, there is a need for an approach to providing a focused display of dense visualized three-dimensional data representing extracted semantic content as a combination of graphical and textual data elements. Preferably, such an approach would provide a user interface facilitating convenient navigation as a heads-up display (HUD) logically provided over the visualized data and would enable large- or fine-grained data navigation, searching and data exploration.